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Running
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Zero
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# Evaluate with Seed-TTS testset
import sys
import os
import argparse
sys.path.append(os.getcwd())
import multiprocessing as mp
from importlib.resources import files
import numpy as np
from f5_tts.eval.utils_eval import (
get_seed_tts_test,
run_asr_wer,
run_sim,
)
rel_path = str(files("f5_tts").joinpath("../../"))
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("-e", "--eval_task", type=str, default="wer", choices=["sim", "wer"])
parser.add_argument("-l", "--lang", type=str, default="en", choices=["zh", "en"])
parser.add_argument("-g", "--gen_wav_dir", type=str, required=True)
parser.add_argument("-n", "--gpu_nums", type=int, default=8, help="Number of GPUs to use")
parser.add_argument("--local", action="store_true", help="Use local custom checkpoint directory")
return parser.parse_args()
def main():
args = get_args()
eval_task = args.eval_task
lang = args.lang
gen_wav_dir = args.gen_wav_dir
metalst = rel_path + f"/data/seedtts_testset/{lang}/meta.lst" # seed-tts testset
# NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
# zh 1.254 seems a result of 4 workers wer_seed_tts
gpus = list(range(args.gpu_nums))
test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
local = args.local
if local: # use local custom checkpoint dir
if lang == "zh":
asr_ckpt_dir = "../checkpoints/funasr" # paraformer-zh dir under funasr
elif lang == "en":
asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
else:
asr_ckpt_dir = "" # auto download to cache dir
wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
# --------------------------- WER ---------------------------
if eval_task == "wer":
wers = []
with mp.Pool(processes=len(gpus)) as pool:
args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
results = pool.map(run_asr_wer, args)
for wers_ in results:
wers.extend(wers_)
wer = round(np.mean(wers) * 100, 3)
print(f"\nTotal {len(wers)} samples")
print(f"WER : {wer}%")
# --------------------------- SIM ---------------------------
if eval_task == "sim":
sim_list = []
with mp.Pool(processes=len(gpus)) as pool:
args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
results = pool.map(run_sim, args)
for sim_ in results:
sim_list.extend(sim_)
sim = round(sum(sim_list) / len(sim_list), 3)
print(f"\nTotal {len(sim_list)} samples")
print(f"SIM : {sim}")
if __name__ == "__main__":
main()
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